A Hybrid Rao-NM Algorithm for Image Template Matching

• Published in: Entropy (Basel, Switzerland) Vol. 23; no. 6; p. 678
• Main Authors: Liu, Xinran, Wang, Zhongju, Wang, Long, Huang, Chao, Luo, Xiong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 27.05.2021; MDPI
• Subjects: Algorithms; computational intelligence; Computing time; Genetic algorithms; image matching; Optimization; Particle swarm optimization; Rao algorithm; Searching; Template matching
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e23060678

This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.